Maternal diets, nutritional status, and zinc in contemporary Mexican infants' teeth: Implications for reconstructing paleodiets
Bibliographic record
Abstract
Despite attempts to use zinc (Zn) concentrations in hard tissues to comment upon the degree of carnivory in past populations, zinc has yielded inconsistent trophic level effects. The question of what, if anything, zinc in human enamel reveals regarding past diets is the focus of this research. We test whether the zinc content of deciduous tooth enamel from contemporary Mexican infants varies by maternal dietary variables such as zinc intake, proportion of animal products consumed, and dietary components that are known to impact zinc absorption. Deciduous teeth were collected from former participants in a longitudinal study of maternal and infant diet and function in highland Mexico. The Zn/Ca ratios of both prenatal and postnatal regions of 37 anterior teeth representing 26 individuals were assessed via laser ablation-inductively coupled plasma-mass spectrometry. Maternal dietary data collected during lactation were not correlated with zinc levels in the early postnatal enamel of infants' teeth, which were forming at the same time. In the case of prenatal enamel, zinc values were correlated with the consumption of foods known to influence Zn bioavailability, most notably tortillas (P = 0.008; r = 0.510), but not with meat consumption. Unexpectedly, women who consumed diets with poor zinc bioavailability during pregnancy gave birth to infants whose prenatal enamel demonstrated the highest Zn/Ca ratios, possibly due to enhanced zinc absorption during pregnancy for those mothers suffering most from long-term micronutrient deficiency. These results would suggest that zinc is not a reliable trophic level indicator.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".